Improvement and Updating of a Cartographic Road Database by Image Analysis Techniques using Multiple Knowledge Sources and Cues

The extraction of roads from digital images has drawn considerable attention lately. The existing approaches cover a wide variety of strategies, using different resolution aerial or satellite images. Overviews can be found in GRUEN et al. (1995, 1997) and FOERSTNER & PLUEMER (1997). Semi-automatic schemes require human interaction to provide interactively some information to control the extraction. Roads are then extracted by profile matching (AIRAULT et al. 1996, VOSSELMAN & DE GUNST 1997), cooperative algorithms (MCKEOWN et al. 1988), and dynamic programming or LSB-Snakes (GRUEN & LI 1997). Automatic methods usually extract reliable hypotheses for road segments through edge and line detection and then establish connections between road segments to form road networks (WANG & TRINDER 2000). Contextual information is taken into account to guide the extraction of roads (RUSKONE 1996). Roads can be detected in multi resolution images (BAUMGARTNER & HINZ 2000). The existing approaches show individually that the use of road models and varying strategies for different types of scenes are promising. However, all the methods are based on relatively simplistic road models, and most of them make only insufficient use of a priori information, thus they are very sensitive to disturbances like cars, shadows or occlusions, and do not always provide good quality results. Furthermore, most approaches work in single 2D images, thus neglecting valuable information inherent in 3D processing.